Abstract
A model based on information theory, which allows yield managers to determine an optimal portfolio of yield analysis technologies for both the R&D and volume production environments, is presented. The information extraction per experimentation cycle and information extraction per unit time serve as benchmarking metrics for yield learning. They enable yield managers to make objective comparisons of apparently unrelated technologies. Combinations of four yield analysis tools - electrical testing, automatic defect classification, spatial signature analysis and wafer position analysis - are examined in detail to determine the relative value of ownership of different yield analysis technologies.
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